"""simple docstring"""


import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase = True  ,       __lowerCAmelCase = math.inf  ,       __lowerCAmelCase = -math.inf  ,       __lowerCAmelCase = math.inf  ,       __lowerCAmelCase = -math.inf  ,       __lowerCAmelCase = False  ,       __lowerCAmelCase = 100  ,       __lowerCAmelCase = 0.01  ,       __lowerCAmelCase = 1  ,       )				->   Any:




						'''simple docstring'''



						lowerCamelCase__   =False
						lowerCamelCase__   =search_prob
						lowerCamelCase__   =start_temperate
						lowerCamelCase__   =[]
						lowerCamelCase__   =0
						lowerCamelCase__   =None

						while not search_end:
												lowerCamelCase__   =current_state.score()
												if best_state is None or current_score > best_state.score():
																		lowerCamelCase__   =current_state
												scores.append(a__							)
												iterations += 1
												lowerCamelCase__   =None
												lowerCamelCase__   =current_state.get_neighbors()
												while (
												    next_state is None and neighbors
												):  # till we do not find a neighbor that we can move to
																		lowerCamelCase__   =random.randint(0  ,       len(a__							) - 1							)  # picking a random neighbor
																		lowerCamelCase__   =neighbors.pop(a__							)
																		lowerCamelCase__   =picked_neighbor.score() - current_score

																		if (
																		    picked_neighbor.x > max_x
																		    or picked_neighbor.x < min_x
																		    or picked_neighbor.y > max_y
																		    or picked_neighbor.y < min_y
																		):
																								continue  # neighbor outside our bounds

																		if not find_max:
																								lowerCamelCase__   =change * -1  # in case we are finding minimum
																		if change > 0:  # improves the solution
																								lowerCamelCase__   =picked_neighbor
																		else:
																								lowerCamelCase__   =(math.e) ** (
																								    change / current_temp
																								)  # probability generation function
																								if random.random() < probability:  # random number within probability
																														lowerCamelCase__   =picked_neighbor
												lowerCamelCase__   =current_temp - (current_temp * rate_of_decrease)

												if current_temp < threshold_temp or next_state is None:
																		# temperature below threshold, or could not find a suitable neighbor
																		lowerCamelCase__   =True
												else:
																		lowerCamelCase__   =next_state

						if visualization:
												from matplotlib import pyplot as plt

												plt.plot(range(a__							)  ,       a__							)
												plt.xlabel("Iterations"							)
												plt.ylabel("Function values"							)
												plt.show()
						return best_state


if __name__ == "__main__":





	def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Any:




							'''simple docstring'''



							return (x**2) + (y**2)

	# starting the problem with initial coordinates (12, 47)
	a				=SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
	a				=simulated_annealing(
	    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
	)
	print(
	    'The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
	    F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
	)

	# starting the problem with initial coordinates (12, 47)
	a				=SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
	a				=simulated_annealing(
	    prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
	)
	print(
	    'The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
	    F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
	)





	def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   str:




							'''simple docstring'''



							return (3 * x**2) - (6 * y)

	a				=SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	a				=simulated_annealing(prob, find_max=False, visualization=True)
	print(
	    'The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
	    F'''{local_min.score()}'''
	)

	a				=SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	a				=simulated_annealing(prob, find_max=True, visualization=True)





	print(
	    'The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
	    F'''{local_min.score()}'''
	)



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					['''image_processor''', '''tokenizer''']
 __A			       =					'''ViTImageProcessor'''
 __A			       =					('''CLIPTokenizer''', '''CLIPTokenizerFast''')







 def __init__(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]=None							, lowercase_				:   Dict=None							, **lowercase_				:   List[str])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."							, lowercase_							, )
     _UpperCamelCase           =    kwargs.pop("feature_extractor")

   _UpperCamelCase           =    image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("You need to specify an `image_processor`.")
   if tokenizer is None:
     raise ValueError("You need to specify a `tokenizer`.")

   super().__init__(lowercase_							, lowercase_)







 def __call__(		self				:   Any							, lowercase_				:   List[str]=None							, lowercase_				:   int=None							, lowercase_				:   Optional[Any]=None							, lowercase_				:   Dict=None							, **lowercase_				:   Dict)						->	int:


   """simple docstring"""







   if text is None and visual_prompt is None and images is None:
     raise ValueError("You have to specify either text, visual prompt or images.")

   if text is not None and visual_prompt is not None:
     raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt.")

   if text is not None:
     _UpperCamelCase           =    self.tokenizer(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if images is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None and images is not None:
     _UpperCamelCase           =    {
         "pixel_values": image_features.pixel_values,
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   elif text is not None and images is not None:
     _UpperCamelCase           =    image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   elif visual_prompt is not None:
     _UpperCamelCase           =    {
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   else:
     return BatchEncoding(data=dict(**lowercase_)							, tensor_type=lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, *lowercase_				:   List[Any]							, **lowercase_				:   List[Any])						->	int:


   """simple docstring"""







   return self.tokenizer.batch_decode(*lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, *lowercase_				:   Any							, **lowercase_				:   int)						->	Optional[int]:


   """simple docstring"""







   return self.tokenizer.decode(*lowercase_							, **lowercase_)







 @property
 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	str:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							, lowercase_							, )
   return self.image_processor_class







 @property
 def 		__UpperCAmelCase				(		self				:   int)						->	int:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							, lowercase_							, )
   return self.image_processor




